En økosystemmodell brukes for å beskrive større eller mindre deler av økosystemene og vekselvirkninger mellom disse. En slik modell kan inkludere en eller flere typer næringssalt, plankton, fisk og sjøpattedyr. 

På samme måte som for spredningsmodeller benyttes en sirkulasjonsmodell for å beskrive det fysiske miljøet, og en økosystemmodell inneholder også ofte en egen spredningsmodell.


NORWECOM.E2E er en samling ulike moduler som skal beskrive livssyklus og samspill mellom nøkkelarter i våre økosystemer. E2E (end-to-end) viser til ambisjonen om å gjøre dette E2E, altså fra de fysiske prosessene (sirkulasjon, temperatur, salt og lys) via plankton til fisk og marine pattedyr. Det fysiske miljøet beregnes ved hjelp av en egen sirkulasjonsmodell, og blir brukt som input i den biologiske modellen. De biologiske komponentene har et høyt detaljnivå for hver art, og beskriver både vekst, fødeopptak, reproduksjon, vandring og død. De biologiske modulene påvirker hverandre gjensidig, og modellen kan derfor brukes til å studere utvikling i enkeltarter og økosystem i perioder over flere år. Visjonen er å lage en E2E modell for Norskehavet som inkluderer de viktigste artene.

NORWECOM.E2E is a merger of several models; an Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model for nutrient cycling and the lower trophic levels (Aksnes et al. 1995; Skogen et al. 1995; Skogen et al. 2007) and different Individual Based Models (IBMs, Grimm and Railsback 2005) developed initially for fish (Huse and Giske 1998; Strand et al. 2002; Huse et al. 2004; Huse and Ellingsen 2008; Utne et al. 2012) and zooplankton (Huse 2005; Samuelsen et al. 2009; Hjøllo et al. 2012; Hjøllo et al. submitted). NORWECOM.E2E is  one of very few bottom-up models world-wide where IBMs for different trophic levels are two-way coupled and used to simulate food web dynamics of a large regional sea, and the only model system to run for the Norwegian Sea. At present, IBMs for adult pelagic fish (mackerel, herring, blue whiting), Calanus finmarchicus, Calanus hyperboreus and krill (Meganyctiphanes norvegica) are running, while there is ongoing work to include IBMs for capelin, mesopelagic fish and fishing vessels. The model system also has modules for ocean acidification (Skogen et al. 2014) and contaminants (Green et al. 2011). Through NORWECOM.E2E, all these models are now being integrated into a fully coupled model system running in offline mode using physical fields (salinity, temperature, velocities, etc.) from the ROMS model (Shchepetkin and McWilliams 2005). The model system has also been used to study climate effects through downscaling of global climate models (Skaret et al., 2014).

The 3D IBMs implemented for Calanus and krill take into account the entire life cycle and main life-history features such as growth, mortality, movement and reproduction, as well as adaptive traits which control the interaction with the environment (Huse 2005). Vertical movement is an emergent property resulting from many generations of evolution using a genetic algorithm. The models are linked so that the IBM receives input on phytoplankton and zooplankton densities from the NPZD model. Both Calanus and krill individuals then feed on the plankton and the local plankton abundance is updated continuously in the model. The krill, considered an omnivorous species, may also feed on the Calanus population. All Calanus biomass removed by foraging krill is continuously added as mortality in the Calanus population so that mass balance is achieved.

The three species of pelagic fish included in the model are herring, blue whiting and mackerel, which are dominant in terms of biomass in the region. The model cycle starts at the overwintering area, followed by spawning migrations, feeding migrations and migrations back to the overwintering area. The model includes the adult and juvenile stage of the life cycle, and there are ongoing work to extend these to full life cycle models. The pelagic fish module includes processes such as movement, feeding, growth and mortality. The pelagic fish are feeding on the Calanus through a two way coupling, and Utne et al. (2012) showed how linking the Calanus IBM with the pelagic fish IBMs changed the spatial distribution of C. finmarchicus in the upper 400 m towards higher abundances in the northern Norwegian Sea and lower values along the Norwegian coast compared to running the Calanus IBM with a uniform fish predation.

Cited literature

Aksnes, D.L., Ulvestad, K.B., Baliño, B.M., Berntsen, J., Egge, J.K., Svendsen, E. (1995) Ecological modeling in coastal waters - Towards predictive physical-chemical-biological simulation-models. Ophelia, 41, 5-36.

Green, N.W., et al (2011). Tilførselsprogrammet 2010. Overvåking av tilførsler og miljøtilstand I Nordsjøen. Technical report TA 2810/2011 (p. 101pp+106app). Oslo, Norway: KLIF.

Grimm, V., Railsback, S. (2005). Individual-based Modeling and Ecology: Princeton University Press.

Hjøllo, S.S., Huse, G., Skogen, M.D., Melle, W. (2012) Modelling secondary production in the Norwegian Sea with a fully coupled physical/primary production/individual-based Calanus finmarchicus model system. Marine Biology Research, 8, 508-526.

Huse, G. (2005) Artificial evolution of Calanus' life history strategies under different predation levels. GLOBEC Newsletter, 11, 19 Huse, G., Ellingsen, I. (2008) Capelin migrations and climate change - a modeling analysis. Climatic Change, 87, 177-197.

Huse, G., Giske, J. (1998) Ecology in Mare Pentium: an individual based spatio-temporal model for fish with adapted behaviour. Fisheries Research (Amsterdam), 37, 163-178.

Huse, G., et al. 2012. Effects of interactions between fish populations on ecosystem dynamics in the Norwegian Sea - results of the INFERNO project Preface. Marine Biology Research, 8: 415-419.

Huse, G., Johansen, G.O., Bogstad, L., Gjøsæter, H. (2004) Studying spatial and trophic interactions between capelin and cod using individual-based modeling. ICES Journal of Marine Science, 61, 1201-1213.

Samuelsen, A., Huse, G., Hansen, C. (2009) Shelf recruitment of Calanus finmarchicus off the west coast of Norway: role of physical processes and timing of diapause termination. Marine Ecology Progress Series, 386, 163-180.

Shchepetkin, A.F., McWilliams, J.C. (2005) The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9, 347-404.

Skaret, G., Dalpadado, P., Hjøllo, S.S., Skogen, M.D.,& Strand, E.(2014). Calanus finmarchicus abundance, production and population dynamics in the Barents Sea in a future climate. Prog. Oceanogr.doi:dx.doi.org/10.1016/j.pocean.2014.04.008

Skogen, M.D., Budgell, W.P., Rey, F. (2007) Interannual variability in Nordic seas primary production. ICES Journal of Marine Science, 64, 889-898.

Skogen, M.D., Olsen, A., Børsheim, K.Y., B., S.A., Skjelvan, I. (2014) Modelling ocean acidification in the Nordic and Barents Seas in present and future climate. Journal of Marine Systems.

Skogen, M.D., Svendsen, E., Berntsen, J., Aksnes, D., Ulvestad, K.B. (1995) Modeling the primary production in the north-sea using a coupled 3-dimensional physical-chemical-biological ocean model. Estuarine Coastal and Shelf Science, 41, 545-565.

Strand, E., Huse, G., Giske, J. (2002) Artificial evolution of life history and behavior. American Naturalist, 159, 624-644.

Utne K.R., Hjøllo S.S., Huse G., Skogen M. 2012. Estimating the consumption of Calanus finmarchicus by planktivorous fish in the Norwegian Sea using a fully coupled 3D model system. Marine Biology Research, 8:5-6, 527-547


Atlantis (Fulton et al. 2007) is a so-called end-to-end model, which means that it includes more or less everything, from physics to fisheries. It has been tested and run actively in several places around the world, (nine model domains around Australia, and at the east and west coast of U.S.) and is at the moment being initialized areas around Hawaii, in the North Sea and, by IMR, in the Barents and Nordic Seas.

This is the first time it will be coupled with ice, and have the large seasonal variations in light as there are in the northern part of the model domain.

The model domain (fig. 1) is made of arbitrary polygons, which are defined based on information about the hydrography, depth and biology. There are 59 polygons, which covers an area of about 4x106 km2. In depth, the model has seven depth layers (0-50 m, 50-150 m, 150-250 m, 250-375 m, 375-500 m, 500-1000 m and 1000-1250). In areas where there are greater depths than 1250 m, the bottom layer can be stretched down to the bottom.

Figure 1


At the moment, the model includes 51 species and functional groups (fig. 2).  All species cannot be included; some have been gathered in functional groups. The gathering has been performed with the aim that the species included in a group should eat similar prey, have similar longevities and be in the same size class. It is not a good idea to group together prey and predators, or species that live for 2 years with one that lives for 40 years.

Figure 2


In addition to the biology, Atlantis also includes fisheries. This means that we will include time-series of catches and by-catches of the species/groups included in the model, in addition to number of vessels, type of gear and when they are at sea.

Atlantis gives great opportunities to run “what-if” scenarios. It can be used to look at the impact of climate, fisheries and pollution on the ecosystem as a whole. In the Barents and Nordic Seas this means that it for instance can be used to look at the vulnerabilities of the “ice-loving” species, like polar bears and seals, in connection to decreasing ice-cover and increasing temperatures. With respect to fisheries, it can give more information about the effect of changing gear, or reducing/increasing the number of vessels in certain areas.


Fulton EA, Smith ADM and Smith DC. 2007. Alternative Management Strategies for Southeast Australian Commonwealth Fisheries: Stage 2: Quantitative Management Strategy Evaluation. Australian Fisheries Management Authority Report.